Efficient Autonomous Robotic Exploration With Semantic Road Map in Indoor Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This letter presents a novel and integrated framework for Next-Best-View (NBV) selection toward autonomous robotic exploration in indoor environments. A topological map, named semantic road map (SRM), is proposed to represent the explored environment during the exploration. The basic concept of the SRM is to construct a graph with nodes containing the exploration states and with edges satisfying the collision-free constraints. Especially, the SRM integrates both semantic and structure information of the environment, which possesses the beneficial properties of using a topological map in the exploration. It is worth noting that the proposed SRM is incrementally built along with the exploration process, thereby, avoiding the unnecessary reconsideration of the explored areas when constructing the topological map. Based on the SRM, a novel decision model with semantic information is presented for determining the NBV during the exploration. Moreover, the decision model takes into account both information gain and cost-to-go of a candidate NBV, which can be queried efficiently on the SRM, enabling the efficient exploration of the environment. The effectiveness and efficiency of the proposed system are assessed and demonstrated using both simulated and real-world indoor experiments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it